肝细胞癌（Hepatocellular carcinoma, HCC）是一种原发性肝恶性肿瘤，精准的肝细胞癌组织学分级判别对病人的治疗方式选择中起着至关重要的作用，然而目前作为金标准的病理检测具有有创性和局部性取样的缺陷，采用影像手段提供无创准确的影像估测，特别是结合人工智能技术，成为热点问题。本文讨论了基于深度学习方法的肝细胞癌组织学诊断模型，并结合放射科医师的临床读片经验，提出了一种基于自注意力指导，使用动态对比增强核磁共振成像（DCE-MRI）序列融合的HCC分级影像估测方法。本方法在学习各序列及其通道重要程度基础上，结合了增强序列所蕴含的时空信息指导多序列的融合计算，有效利用了丰富且重要的信息从而提升了分类效果。在来自三甲医院的临床数据集上进行实验，实验结果表明，本文所提出的基于自注意力指导的多参数序列3D时空融合模型取得相比几种基准和主流模型最高的分类性能，在WHO组织学分级任务中，所提出模型的分类准确度达到80％，灵敏度为82％，准确度为82％。
Hepatocellular carcinoma (HCC) is a primary liver malignant tumor with a high recurrence rate and poor prognosis, even after resection or liver transplantation, there is still a great possibility of recurrence. The accurate identification of the histological grade of hepatocellular carcinoma plays a vital role in the choice of the patient's treatment. In this paper, we discussed the histological diagnosis model of hepatocellular carcinoma based on deep learning methods and the clinical diagnosis experience of radiologists. We proposed the "self-attention" scheme of the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images and the corresponding model which can calculate and utilize the "self-attention" weights for the HCC histological grade classification task. The innovation of this method lies in calculating the importance of each sequence and each channel for the final classification task and combining the calculated weights with the spatiotemporal information contained in the enhanced sequence which can improve the classification performance. We conduct experiments on the self-built dataset based on clinical data. The experimental results show that the classification performance of our proposed "self-attention" model is better than that of the baseline and benchmark models in both the WHO and the Edmonson HCC histological grading standard. The classification accuracy of the "self-attention" model reaches 80%, the sensitivity is 82%, and the accuracy is 82%.